Discriminative Distance-Based Network Indices with Application to Link Prediction

In large networks, using the length of shortest paths as the distance measure has shortcomings. A well-studied shortcoming is that extending it to disconnected graphs and directed graphs is controversial. The second shortcoming is that a huge number of vertices may have exactly the same score. The third shortcoming is that in many applications, the distance between two vertices not only depends on the length of shortest paths, but also on the number of shortest paths. In this paper, first we develop a new distance measure between vertices of a graph that yields discriminative distance-based centrality indices. This measure is proportional to the length of shortest paths and inversely proportional to the number of shortest paths. We present algorithms for exact computation of the proposed discriminative indices. Second, we develop randomized algorithms that precisely estimate average discriminative path length and average discriminative eccentricity and show that they give $(\epsilon,\delta)$-approximations of these indices. Third, we perform extensive experiments over several real-world networks from different domains. In our experiments, we first show that compared to the traditional indices, discriminative indices have usually much more discriminability. Then, we show that our randomized algorithms can very precisely estimate average discriminative path length and average discriminative eccentricity, using only few samples. Then, we show that real-world networks have usually a tiny average discriminative path length, bounded by a constant (e.g., 2). Fourth, in order to better motivate the usefulness of our proposed distance measure, we present a novel link prediction method, that uses discriminative distance to decide which vertices are more likely to form a link in future, and show its superior performance compared to the well-known existing measures

A Heuristic for Distance Fusion in Cover Song Identification

In this paper, we propose a method to integrate the results of different cover song identification algorithms into one single measure which, on the average, gives better results than initial algorithms. The fusion of the different distance measures is made by projecting all the measures in a multi-dimensional space, where the dimensionality of this space is the number of the considered distances. In our experiments, we test two distance measures, namely the Dynamic Time Warping and the Qmax measure when applied in different combinations to two features, namely a Salience feature and a Harmonic Pitch Class Profile (HPCP). While the HPCP is meant to extract purely harmonic descriptions, in fact, the Salience allows to better discern melodic differences. It is shown that the combination of two or more distance measure improves the overall performance

Quantifying causal influences

Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between $n$ variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other $n-1$ variables. However, quantifying the causal influence of one variable on another one remains a nontrivial question. Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy. We then introduce a communication scenario, where edges in a DAG play the role of channels that can be locally corrupted by interventions. Causal strength is then the relative entropy distance between the old and the new distribution. Many other measures of causal strength have been proposed, including average causal effect, transfer entropy, directed information, and information flow. We explain how they fail to satisfy the postulates on simple DAGs of $\leq3$ nodes. Finally, we investigate the behavior of our measure on time-series, supporting our claims with experiments on simulated data.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1145 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Average observational quantities in the timescape cosmology

We examine the properties of a recently proposed observationally viable alternative to homogeneous cosmology with smooth dark energy, the timescape cosmology. In the timescape model cosmic acceleration is realized as an apparent effect related to the calibration of clocks and rods of observers in bound systems relative to volume-average observers in an inhomogeneous geometry in ordinary general relativity. The model is based on an exact solution to a Buchert average of the Einstein equations with backreaction. The present paper examines a number of observational tests which will enable the timescape model to be distinguished from homogeneous cosmologies with a cosmological constant or other smooth dark energy, in current and future generations of dark energy experiments. Predictions are presented for: comoving distance measures; H(z); the equivalent of the dark energy equation of state, w(z); the Om(z) measure of Sahni, Shafieloo and Starobinsky; the Alcock-Paczynski test; the baryon acoustic oscillation measure, D_v; the inhomogeneity test of Clarkson, Bassett and Lu; and the time drift of cosmological redshifts. Where possible, the predictions are compared to recent independent studies of similar measures in homogeneous cosmologies with dark energy. Three separate tests with indications of results in possible tension with the Lambda CDM model are found to be consistent with the expectations of the timescape cosmology.Comment: 22 pages, 12 figures; v2 discussion, references added, matches published versio

A Ranking Distance Based Diversity Measure for Multiple Classifier Systems

International audienceMultiple classifier fusion belongs to the decision-level information fusion, which has been widely used in many pattern classification applications, especially when the single classifier is not competent. However, multiple classifier fusion can not assure the improvement of the classification accuracy. The diversity among those classifiers in the multiple classifier system (MCS) is crucial for improving the fused classification accuracy. Various diversity measures for MCS have been proposed, which are mainly based on the average sample-wise classification consistency between different member classifiers. In this paper, we propose to define the diversity between member classifiers from a different standpoint. If different member classifiers in an MCS are good at classifying different classes, i.e., there exist expert-classifiers for each concerned class, the improvement of the accuracy of classifier fusion can be expected. Each classifier has a ranking of classes in term of the classification accuracies, based on which, a new diversity measure is implemented using the ranking distance. A larger average ranking distance represents a higher diversity. The new proposed diversity measure is used together with each single classifier's performance on training samples to design and optimize the MCS. Experiments, simulations , and related analyses are provided to illustrate and validate our new proposed diversity measure

Efficient and Robust Detection of Duplicate Videos in a Database

In this paper, the duplicate detection method is to retrieve the best matching model video for a given query video using fingerprint. We have used the Color Layout Descriptor method and Opponent Color Space to extract feature from frame and perform k-means based clustering to generate fingerprints which are further encoded by Vector Quantization. The model-to-query video distance is computed using a new distance measure to find the similarity. To perform efficient search coarse-to-fine matching scheme is used to retrieve best match. We perform experiments on query videos and real time video with an average duration of 60 sec; the duplicate video is detected with high similarity

Context-aware-based Location Recommendation for Elderly Care

As adults age, the body declines. Living independently at home can be a significant challenge for the elderly, particularly for those who suffer from dementia or who have memory impairment. Assisting the elderly to live independently and safely in their own homes by providing appropriate services for them and ensuring that caregivers are immediately alerted in the event of an emergency is crucial. Utilizing context in the recommendation process will make recommendations more appropriate. A model of a context-aware-based location recommender system that can seamlessly monitor the location of the elderly and deliver appropriate location recommendations by considering context is proposed as our contribution. Two scenarios are investigated: (1) we classify location as follows: bedroom (class 1), dining room (class 2), and living room (class 3); (2) we classify location as follows: inside (class 1) and outside (class 2) the bedroom. We evaluate our proposed model using a distance measure concept by employing the cosine distance method. We compare the cosine distance method with fuzzy inference system (FIS) rules on labeled data. The results of the experiments for the first scenario show that the cosine distance has better average accuracy than the fuzzy inference system. For the second scenario, fuzzy c-means (FCM) has the same average accuracy as cosine distance. FCM has slightly better accuracy in class 1 compared to cosine distance (1% difference in accuracy), whereas cosine distance has slightly better accuracy in class 2 compared to the FCM (1% difference in accuracy). In general, we can draw the conclusion that, on this dataset, cosine distance which uses a simple algorithm produced better results than the fuzzy inference system which uses a more complex algorithm.